25 citations · 77 across the 32 of their papers we have counts for
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A Flexible Gradient Tracking Algorithmic Framework for Decentralized Optimization
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
In decentralized optimization over networks, each node in the network has a portion of the global objective function and the aim is to collectively optimize this function. Gradient…
Non-Uniform Smoothness for Gradient Descent
Albert S. Berahas, Lindon Roberts, Fred Roosta
The analysis of gradient descent-type methods typically relies on the Lipschitz continuity of the objective gradient. This generally requires an expensive hyperparameter tuning pro…
Adaptive Consensus: A network pruning approach for decentralized optimization
Suhail M. Shah, Albert S. Berahas, Raghu Bollapragada
We consider network-based decentralized optimization problems, where each node in the network possesses a local function and the objective is to collectively attain a consensus sol…
Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design
Xubo Yue, Raed Al Kontar, Albert S. Berahas +2
Optimal design is a critical yet challenging task within many applications. This challenge arises from the need for extensive trial and error, often done through simulations or run…
Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization
Albert S. Berahas, Raghu Bollapragada, Shagun Gupta
Gradient tracking methods have emerged as one of the most popular approaches for solving decentralized optimization problems over networks. In this setting, each node in the networ…
A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimization
Albert S. Berahas, Miaolan Xie, Baoyu Zhou
A step-search sequential quadratic programming method is proposed for solving nonlinear equality constrained stochastic optimization problems. It is assumed that constraint functio…